Immersive Technologies in Media: Towards the Concept of Generative Mediatization?

The practices of so-called immersive media have been developing in the past few years. The immersive media situation characteristics, infrastructure, content and social aspects have been identified through the use of a multilevel structural and functional methodology, and make it possible to fix its specificity at all levels. The new format of the immersive media situation leads to changes in approaches to the mediatization studies. In the article, to study the media immersive communicative situation a generative approach is proposed for the first time. It is relevant to topological thinking, and to the modern immanent picture of the world, when a person and technology co-create a new form. Along with the generative approach and generative design, the necessity of applying relevant paradigms and methods of psychology to form new theoretical and methodological foundations of immersive user-centric media communication is substantiated. Several new concepts and terms are introduced, in particular, the term immersive hypermediation, which is opposite to immediacy as a classical criterion of media effectiveness. As a result, the analysis of the essence and features of immersive media projects allows fixing the premises of immersive paradigm shift in mediatization studies.

A Study of Grammatical Case Forms and their Directionality in Fulfulde: The Transformational Generative Approach

Case Theory interacts with Government Theory in its operation and so, cases are assigned to the complements of governors. Case assigners are the governors of their dependent clauses while the case receivers are the governed NPs. So, the purpose of the study is to survey case assignment in Fulfulde generally by identifying and analysing the elements of Fulfulde structures and their relationship in terms of structural case. Unstructured observation was the method used for eliciting data for this study. Adequate and natural data were recorded and analysed sentence by sentence, the way they were uttered by the native speakers. The Theoretical Framework adopted for data analysis by this study is Principle and Parameters Theory. The study discovered that in Government, apart from (V)erbs, (P)repositions and tensed INFL, (N)ouns, (A)djectives and Focus Markers FMs can also govern and assign case to their complements in Fulfulde. The study has proved that in Fulfulde, cases can be assigned either to the left or to the right, depending on the relation.

NeuralTPL: a deep learning approach for efficient reaction space exploration

Computer-aided synthesis planning (CASP) has been helping chemists to synthesize novel molecules at an accelerated pace. The recent integration of deep learning with CASP has opened up new avenues for digitizing and exploring the vastly unknown chemical space, and has led to high expectations for fully automated synthesis plannings using machine-discovered novel reactions in the "future". Despite many progresses in the past few years, most deep-learning methods only focus on improving few aspects of CASP (e.g., top-k accuracy). In this work, we target specifically the efficiency of reaction space exploration and its impact on CASP. We propose NeuralTPL, a template-oriented generative approach, that performs impressively across a range of evaluation metrics including chemical validity, diversity, and novelty for various tasks in CASP. In addition, our Transformer-based model bears the potential to learn the core reaction transformation as it can efficiently explore the reaction space. We then perform several experiments and conduct a thorough analysis regarding the three metrics and demonstrate its chemical value for improving the existing deep-learning-driven CASP algorithms.

NeuralTPL: a deep learning approach for efficient reaction space exploration

Computer-aided synthesis planning (CASP) has been helping chemists to synthesize novel molecules at an accelerated pace. The recent integration of deep learning with CASP has opened up new avenues for digitizing and exploring the vastly unknown chemical space, and has led to high expectations for fully automated synthesis plannings using machine-discovered novel reactions in the "future". Despite many progresses in the past few years, most deep-learning methods only focus on improving few aspects of CASP (e.g., top-k accuracy). In this work, we target specifically the efficiency of reaction space exploration and its impact on CASP. We propose NeuralTPL, a template-oriented generative approach, that performs impressively across a range of evaluation metrics including chemical validity, diversity, and novelty for various tasks in CASP. In addition, our Transformer-based model bears the potential to learn the core reaction transformation as it can efficiently explore the reaction space. We then perform several experiments and conduct a thorough analysis regarding the three metrics and demonstrate its chemical value for improving the existing deep-learning-driven CASP algorithms.

On the question of a method for researching user requirements for software

Методы User Story все чаще используются в качестве основы артефактов проектирования требований при разработке программного обеспечения. На практике доказано, что метод User Story является более эффективным для описания основных целей системы. Но непрерывное управление работой программного обеспечения может быть особенно трудоемким и подверженным ошибкам, особенно при оценке качества или объема пользовательских историй и наблюдении за общей картиной системы. С другой стороны, эти модели были признаны эффективными инструментами коммуникации и анализа цели. В рамках данной работы рассмотрены и проанализированы методы выявления и представления требований к разработке программного обеспечения. В статье предлагается генеративный подход для создания диаграмм надежности на основе автоматизированного анализа пользовательских историй. Истории преобразуются в диаграммы, что позволяет разработчикам требований и пользователям проверять основные концепции и функциональные этапы, лежащие в основе историй, и обнаруживать искаженные или избыточные истории. Такие модели также открывают двери для автоматизированного систематического анализа. The User Story methods (user stories) increasingly are used as the basis of requirements design artifacts in software development. In practice, it is proved that the User Story method is more effective for describing the main goals of the system. But continuous management of software operation can be particularly time-consuming and error-prone, especially when evaluating the quality or volume of user stories and observing the overall picture of the system. On the other hand, these models were recognized as effective tools for communication and goal analysis. Within the framework of this work, methods for identifying and presenting requirements for software development are considered and analyzed. In the article, we propose a generative approach for creating reliability diagrams based on automated analysis of user stories. Stories are converted into diagrams, which allow requirements developers and users to check the basic concepts and functional stages underlying the stories, and detect distorted or redundant stories. Such models also open the door for automated systematic analysis.

Expressing Personality Through Non-verbal Behaviour in Real-Time Interaction

The attribution of traits plays an important role as a heuristic for how we interact with others. Many psychological models of personality are analytical in that they derive a classification from reported or hypothesised behaviour. In the work presented here, we follow the opposite approach: Our personality model generates behaviour that leads an observer to attribute personality characteristics to the actor. Concretely, the model controls all relevant aspects of non-verbal behaviour such as gaze, facial expression, gesture, and posture. The model, embodied in a virtual human, affords to realistically interact with participants in real-time. Conceptually, our model focuses on the two dimensions of extra/introversion and stability/neuroticism. In the model, personality parameters influence both, the internal affective state as well as the characteristic of the behaviour execution. Importantly, the parameters of the model are based on empirical findings in the behavioural sciences. To evaluate our model, we conducted two types of studies. Firstly, passive experiments where participants rated videos showing variants of behaviour driven by different personality parameter configurations. Secondly, presential experiments where participants interacted with the virtual human, playing rounds of the Rock-Paper-Scissors game. Our results show that the model is effective in conveying the impression of the personality of a virtual character to users. Embodying the model in an artificial social agent capable of real-time interactive behaviour is the only way to move from an analytical to a generative approach to understanding personality, and we believe that this methodology raises a host of novel research questions in the field of personality theory.

Naming and Cosmology: The Role of Names in the Onto-Generative Process

The article takes the excavated cosmological texts as a basis for reinterpreting the relationship between cosmology, epistemology, and action in Warring States period thought, by focusing on the role of names in situatedness and self-actualization of being. It proposes to view the speculative and the practical concerns in terms of a dynamic union of the receptive and the creative within the onto-generative cycle. Building on Chung-ying Cheng’s onto-generative approach and Heidegger’s hermeneutics of Dasein in Sein und Zeit, the article identifies names as the centre (Gadamer’s Mitte) in which the receptive and the creative aspect of being come together.